We model this as a program synthesis problem where the analogous relationships (spatial transformations) are modeled as compositions of elementary primitives. We employ a variation on the neural algorithmic reasoning
[1]} framework to replace elementary symbolic transformations
with equivalent learned neural network transforms which manipulate latent representations of the input data. While [1]} propose learning a representation together with neural algorithms acting on abstract data, we decouple representation learning from transform learning, first learning a latent representation that captures key information from the input followed by learning transforms inside this
representation space for which input-output examples have been given.
